Harrie Oosterhuis

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 80

Top Topics

Online Learning

Recommender Systems

Learning To Rank

Top Venues

Publications

Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? CoRR (2024)
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. KDD (2024)
Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees. CoRR (2024)
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
A First Look at Selection Bias in Preference Elicitation for Recommendation. CoRR (2024)
Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis, Maarten de Rijke
Optimal Baseline Corrections for Off-Policy Contextual Bandits. CoRR (2024)
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis
Unbiased Learning to Rank: On Recent Advances and Practical Applications. WSDM (2024)
Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. SIGIR (2024)
Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, Maarten de Rijke
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems. CoRR (2024)
Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees. SIGIR (2024)
Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank? ECIR (4) (2024)
Harrie Oosterhuis, Rolf Jagerman, Zhen Qin, Xuanhui Wang, Michael Bendersky
Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I. CoRR (2024)
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions. CoRR (2024)
Le Yan, Zhen Qin, Honglei Zhuang, Rolf Jagerman, Xuanhui Wang, Michael Bendersky, Harrie Oosterhuis
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing. CoRR (2024)
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank. CoRR (2024)
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback. ICTIR (2023)
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. SIGIR (2023)
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. CoRR (2023)
Norman Knyazev, Harrie Oosterhuis
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. CoRR (2023)
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. CoRR (2023)
Norman Knyazev, Harrie Oosterhuis
A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions. RecSys (2023)
Shashank Gupta, Philipp K. Hager, Harrie Oosterhuis
Recent Advancements in Unbiased Learning to Rank. FIRE (2023)
Harrie Oosterhuis
Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank. ACM Trans. Inf. Syst. 41 (3) (2023)
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, Harrie Oosterhuis
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. SIGIR (2023)
Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile, Yixin Wang
CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys (2023)
Norman Knyazev, Harrie Oosterhuis
The Bandwagon Effect: Not Just Another Bias. ICTIR (2022)
Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, Flavian Vasile
CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. RecSys (2022)
Norman Knyazev, Harrie Oosterhuis
The Bandwagon Effect: Not Just Another Bias. CoRR (2022)
Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. SIGIR (2022)
Jin Huang, Harrie Oosterhuis, Maarten de Rijke
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. WSDM (2022)
Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. AAAI (2022)
Harrie Oosterhuis
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract). IJCAI (2022)
Harrie Oosterhuis
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank. CoRR (2022)
Harrie Oosterhuis
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation with Minimal Computational Complexity. SIGIR (2022)
Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker
Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. CoRR (2022)
Harrie Oosterhuis
Doubly-Robust Estimation for Unbiased Learning-to-Rank from Position-Biased Click Feedback. CoRR (2022)
Harrie Oosterhuis
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient Estimation With Minimal Computational Complexity. CoRR (2022)
Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke
State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. CoRR (2022)
Clara Rus, Jeffrey Luppes, Harrie Oosterhuis, Gido H. Schoenmacker
Closing the Gender Wage Gap: Adversarial Fairness in Job Recommendation. HR@RecSys (2022)
Harrie Oosterhuis
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank. ICTIR (2022)
Harrie Oosterhuis, Maarten de Rijke
Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank. WWW (2021)
Harrie Oosterhuis
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. SIGIR (2021)
Harrie Oosterhuis, Maarten de Rijke
Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. WSDM (2021)
Harrie Oosterhuis, Maarten de Rijke
Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank. CoRR (2021)
Harrie Oosterhuis, Maarten de Rijke
Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions (Extended Abstract). IJCAI (2021)
Harrie Oosterhuis
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness. CoRR (2021)
Jin Huang, Harrie Oosterhuis, Maarten de Rijke
It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences Are Dynamic. CoRR (2021)
Harrie Oosterhuis
Session details: Session 4B - Semantic Retrieval. ICTIR (2021)
Harrie Oosterhuis, Maarten de Rijke
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. ICTIR (2020)
Harrie Oosterhuis
Learning from User Interactions with Rankings: A Unification of the Field. CoRR (2020)
Harrie Oosterhuis
Learning from user interactions with rankings: a unification of the field. SIGIR Forum 54 (2) (2020)
Harrie Oosterhuis, Maarten de Rijke
Policy-Aware Unbiased Learning to Rank for Top-k Rankings. CoRR (2020)
Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof
Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. RecSys (2020)
Harrie Oosterhuis, Maarten de Rijke
Unifying Online and Counterfactual Learning to Rank. CoRR (2020)
Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. CIKM (2020)
Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Unbiased Learning to Rank: Counterfactual and Online Approaches. WWW (Companion Volume) (2020)
Ali Vardasbi, Harrie Oosterhuis, Maarten de Rijke
When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. CoRR (2020)
Harrie Oosterhuis, Maarten de Rijke
Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. CoRR (2020)
Harrie Oosterhuis, Maarten de Rijke
Policy-Aware Unbiased Learning to Rank for Top-k Rankings. SIGIR (2020)
Harrie Oosterhuis, Maarten de Rijke
Optimizing Ranking Models in an Online Setting. CoRR (2019)
Harrie Oosterhuis, Maarten de Rijke
Optimizing Ranking Models in an Online Setting. ECIR (1) (2019)
Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. SIGIR (2019)
Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions. CoRR (2019)
Ana Lucic, Harrie Oosterhuis, Hinda Haned, Maarten de Rijke
Actionable Interpretability through Optimizable Counterfactual Explanations for Tree Ensembles. CoRR (2019)
Claudio Lucchese, Franco Maria Nardini, Rama Kumar Pasumarthi, Sebastian Bruch, Michael Bendersky, Xuanhui Wang, Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning. SIGIR (2019)
Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Unbiased Learning to Rank: Counterfactual and Online Approaches. CoRR (2019)
Harrie Oosterhuis, Maarten de Rijke
Ranking for Relevance and Display Preferences in Complex Presentation Layouts. SIGIR (2018)
Harrie Oosterhuis, Maarten de Rijke
Differentiable Unbiased Online Learning to Rank. CoRR (2018)
Ziming Li, Artem Grotov, Julia Kiseleva, Maarten de Rijke, Harrie Oosterhuis
Optimizing Interactive Systems with Data-Driven Objectives. CoRR (2018)
Harrie Oosterhuis, Maarten de Rijke
Differentiable Unbiased Online Learning to Rank. CIKM (2018)
Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke
The Potential of Learned Index Structures for Index Compression. ADCS (2018)
Harrie Oosterhuis, J. Shane Culpepper, Maarten de Rijke
The Potential of Learned Index Structures for Index Compression. CoRR (2018)
Harrie Oosterhuis, Maarten de Rijke
Ranking for Relevance and Display Preferences in Complex Presentation Layouts. CoRR (2018)
Harrie Oosterhuis, Maarten de Rijke
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. CIKM (2017)
Harrie Oosterhuis, Maarten de Rijke
Sensitive and Scalable Online Evaluation with Theoretical Guarantees. CoRR (2017)
Harrie Oosterhuis, Maarten de Rijke
Sensitive and Scalable Online Evaluation with Theoretical Guarantees. CIKM (2017)
Harrie Oosterhuis, Maarten de Rijke
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval. CoRR (2017)
Rolf Jagerman, Harrie Oosterhuis, Maarten de Rijke
Query-Level Ranker Specialization. LEARNER@ICTIR (2017)
Harrie Oosterhuis, Sujith Ravi, Michael Bendersky
Semantic Video Trailers. CoRR (2016)
Anne Schuth, Harrie Oosterhuis, Shimon Whiteson, Maarten de Rijke
Multileave Gradient Descent for Fast Online Learning to Rank. WSDM (2016)
Harrie Oosterhuis, Anne Schuth, Maarten de Rijke
Probabilistic Multileave Gradient Descent. ECIR (2016)
Anne Schuth, Robert-Jan Bruintjes, Fritjof Buüttner, Joost van Doorn, Carla Groenland, Harrie Oosterhuis, Cong-Nguyen Tran, Bas Veeling, Jos van der Velde, Roger Wechsler, David Woudenberg, Maarten de Rijke
Probabilistic Multileave for Online Retrieval Evaluation. SIGIR (2015)